Performance Guarantees for Web Server End-Systems: A Control-Theoretical Approach
IEEE Transactions on Parallel and Distributed Systems
Systems That Know What They're Doing
IEEE Intelligent Systems
The Vision of Autonomic Computing
Computer
Multi-Machine Scheduling - A Multi-Agent Learning Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
A knowledge plane for the internet
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Competitive algorithms for the dynamic selection of component implementations
IBM Systems Journal
Failure Diagnosis Using Decision Trees
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
File Classification in Self-* Storage Systems
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Utility Functions in Autonomic Systems
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Research challenges of autonomic computing
Proceedings of the 27th international conference on Software engineering
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet Computing
Scheduling for Reliable Execution in Autonomic Systems
ATC '08 Proceedings of the 5th international conference on Autonomic and Trusted Computing
Designing fuzzy-genetic learner model based on multi-agent systems in supply chain management
Expert Systems with Applications: An International Journal
Scheduling policy design for autonomic systems
International Journal of Autonomous and Adaptive Communications Systems
Towards autonomic computing systems
Engineering Applications of Artificial Intelligence
A note on the learning effect in multi-agent optimization
Expert Systems with Applications: An International Journal
Smart data structures: an online machine learning approach to multicore data structures
Proceedings of the 8th ACM international conference on Autonomic computing
Information Sciences: an International Journal
Computers and Operations Research
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Computer systems are rapidly becoming so complex that maintaining them with human support staffs will be prohibitively expensive and inefficient. In response, visionaries have begun proposing that computer systems be imbued with the ability to configure themselves, diagnose failures, and ultimately repair themselves in response to these failures. However, despite convincing arguments that such a shift would be desirable, as of yet there has been little concrete progress made towards this goal. These challenges are naturally suited to machine learning methods. Hence, this article presents a new network simulator designed to study the application of machine learning methods from a system-wide perspective. Also, learning-based methods for addressing the problems of job routing and CPU scheduling in the simulated networks are introduced. Experimental results verify that methods using machine learning outperform reasonable heuristic and hand-coded approaches on example networks designed to capture many of the complexities that exist in real systems.